Energy information of an outputted pulse of an ionizing radiation detector is a kind of basic information to be obtained in the field of ionizing radiation detection, which has a plenty of uses, including distinguishing the type of a ray in ionizing radiation detection, judging whether a ray is scattered by a substance in the field of nuclear medicine imaging, and determining a deposition position of a ray in a detector of a location-sensitive type photoelectric device. In an ionizing radiation detector, for the same detector, usually amplitude of an electric pulse signal output by a detector is linear to energy deposited in the detector by a ray, and a rise time and a fall time of a pulse are both constants. So normally an area surrounded by a pulse signal waveform and the time axis (that is to count the quantity of the total charges generated by a ray in a detector) is used to represent energy of an ionizing radiation source.
There are two traditional pulse energy acquisition methods for an ionizing radiation detector. A first method is to utilize a charge integral circuit to collect charge carried by a pulse outputted by a detector, and then use a slow speed Analog-to-Digital Converter (hereafter referred to as ADC) to sample the maximum charge quantity stored in a capacitor in the integral circuit to represent the energy value deposited in the detector by a ray. A second method is to perform integral shaping on a pulse outputted by a detector to form a relative slow speed signal, then perform slow speed ADC sampling, and finally perform numerical integration on sampling points to obtain the energy of a pulse. The integration process in the traditional energy acquisition methods limits the maximum counting rate of a system. In addition, an analog integral shaping circuit is easily influenced by external factors like temperature and so on, thereby leading to the performance changing with environments. Furthermore, a parameter of an analog circuit needs to be adjusted according to a specific application, so that the correction and maintenance of a system becomes considerable difficult. By using a high speed ADC to directly digitalize an electric pulse signal outputted by a detector, the defects of the traditional methods can be solved, but problems of high cost and high power consumption are brought. Moreover, the high speed ADC has higher requirements for a back end processing speed and a transmission bandwidth, which increases the difficulty of designing a back end processing circuit.
In order to solve bottleneck problems of the high counting energy acquisition, some scholar put forward a prior information-based undersampling pulse energy acquisition method. The core of the method lies in that by utilizing prior information including a physical model of a pulse, pulse characteristics, etc. of an electric pulse outputted by a detector, a pulse signal can be rebuilt by obtaining only a few of sampling points and solving a maximum-likelihood solution with given sampling points based on principle of statistics or curve fitting, thereby obtaining the pulse energy information. Typical prior information-based undersampling pulse energy acquisition methods include (1) a multi-voltage threshold (MVT) method, (2) an ADC sampling points fitting method, (3) an empirical Bayesian energy estimation method, and a TOT method.
The multi-voltage threshold method includes: presetting multiple voltage thresholds; inputting a pulse signal and the voltage thresholds respectively into two ports of a comparator; measuring the time when a turnover logic pulse is outputted by the comparator, where the time value and a corresponding voltage threshold compose an MVT sampling point; after rebuilding the pulse through curve fitting using the MVT sampling points and pulse signal prior information, obtaining pulse energy information by means of definite integration or by numerical integration after resampling (for detailed information, refer to the reference document: Qingguo Xie, Chien-Min Kao, Zekai Hsiau, and Chin-Tu Chen, “A New Approach for Pulse Processing in Positron Emission Tomography”, IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL.52, NO.4, AUGUST 2005).
The ADC sampling points fitting method includes: using a relative slow speed ADC to sample a pulse signal to obtain multiple ADC sampling points; after rebuilding the pulse through curve fitting using the sampling points and pulse signal prior information, obtaining pulse energy information by means of definite integration or by numerical integration after resampling (for detailed information, refer to the reference document: Nan Zhang, Niraj Doshi, Mehmet Aykac, Ronald Grazioso, Michael Loope, Greg Givens, Lars Eriksson, Florian Bauer, John Young, and Matthias Schmand, “A Pulse Shape Restore Method for Event Localization in PET Scintillation Detection”, Nuclear Science Symposium Conference Record, Vol.7, 2004).
The empirical Bayesian energy estimation method includes: solving a maximum-likelihood solution of given numeral samples as estimated pulse energy information by utilizing Bayesian theory and independence assumption after acquiring pulse digital sampling points (for detailed information, refer to the reference document: Zhenzhou Deng, Qingguo Xie, “Empirical Bayesian energy estimation for Multi-Voltage Threshold digitizer in PET”, Nuclear Science Symposium and Medical Imaging Conference, 2013 IEEE).
The TOT method includes: estimating an energy expected value of a pulse as pulse energy information by fitting the relationship between TOT and pulse energy (for detailed information, refer to the reference document: D. Nygren, “Converting vice to virtue: can time-walk be used as a measure of deposited charge in silicon detectors?”, Internal LBL note, May 1991).
In a prior information-based undersampling pulse energy acquisition system, an accurate pulse model and pulse characteristics description is an important condition to obtain pulse information precisely. However in practice, a pulse physical model and pulse characteristics are not only determined by a detector, but also relevant to a distribution parameter of a reading circuit of the pulse. Therefore it is very difficult to build an accurate pulse model and describe accurate pulse characteristics. When the pulse model and characteristic parameter applied in the process of practical fitting or estimation deviate from an ideal model and a characteristic parameter, the pulse energy information obtained based on prior information may have errors.
Therefore, it is necessary to put forward a method for digitalizing a scintillation pulse in ionizing radiation detection to overcome the above defects.